Why Questionnaire Scores Are Not Measures
Bibliographic record
Abstract
ABSTRACT: Any person is provided by characteristics that can be neither located in body parts nor directly observed (so-called latent variables): these may be behaviors, attitudes, perceptions, motor and cognitive skills, knowledge, emotions, and the like. Physical and rehabilitation medicine frequently faces variables of this kind, the target of many interventions. Latent variables can only be observed through representative behaviors (e.g., walking for independence, moaning for pain, social isolation for depression, etc.). To measure them, behaviors are often listed and summated as items in cumulative questionnaires ("scales"). Questionnaires ultimately provide observations ("raw scores") with the aspect of numbers. Unfortunately, they are only a rough and often misleading approximation to true measures for various reasons. Measures should satisfy the same measurement axioms of physical sciences. In the article, the flaws hidden in questionnaires' scores are summarized, and their consequences in outcome assessment are highlighted. The report should inspire a critical attitude in the readers and foster the interest in modern item response theory, with reference to Rasch analysis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.303 | 0.702 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".